Agentic programming, but more specifically Claude Code, has changed my life. Specs file Like “everyone”, I vibe-code a bit for small tools or parts of programs that don’t require a lot of thought, but what I mostly do is discuss with the model. If it’s something that requires some extensive research, I use Claude (web) to discover the state of the art around the topic. But for most features, I skip this part. ...
Recruiting is hard. In my previous company, habx, I was very proud of the team I built. Most of them stayed for the five years I was there, and they were consistently productive, deeply engaged, and genuinely collaborative. We had something special. Interestingly, the people I ended up being the happiest with were the easiest to recruit. From the moment I met them, I was immediately convinced they were the right fit. My biggest concern wasn’t whether they could do the job, it was how to convince them to join us. Fortunately, I succeeded most of the time. ...
TA-Lib and Github Actions TA-Lib is a popular C-based technical analysis library, often used with a Python wrapper. GitHub Actions is a CI/CD platform to automate workflows. This blog uses it for automation. CI Steps for TA-Lib Add the following steps to your CI workflow: - name: Cache TA-Lib id: cache-talib uses: actions/cache@v2 with: path: ta-lib key: ${{ runner.os }}-talib - name: Build TA-Lib if: steps.cache-talib.outputs.cache-hit != 'true' run: | curl -L http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz -o ta-lib-0.4.0-src.tar.gz tar -xzf ta-lib-0.4.0-src.tar.gz cd ta-lib ./configure --prefix=/usr make - name: Install TA-Lib run: | cd ta-lib sudo make install Summary Cache TA-Lib: Speeds up builds by avoiding redundant downloads. Build TA-Lib: Downloads and compiles the source if not cached. Install TA-Lib: Installs the compiled library. This setup reduces CI runtime by caching and reusing builds.
Introduction This post highlights some powerful features of PostgreSQL, based on our team’s experience at Medici where we implemented architectures using both DynamoDB and PostgreSQL. Features JSON Support One of the standout features of PostgreSQL is its support for JSON data through the json and jsonb types. This feature allows the flexibility of storing structured data without forcing a rigid schema. However, it’s still generally a good idea to prefer a normalized relational design for core data. ...
After two years of building production systems on AWS, here are the patterns and pitfalls I’ve encountered. What works well Managed infrastructure reduces operational burden. Services like RDS, DynamoDB, and S3 handle replication, backups, and scaling automatically. This lets small teams run systems that would otherwise require dedicated operations staff. Core services are battle-tested: S3: Effectively unlimited storage with 99.999999999% durability. The pricing model (pay per GB stored and transferred) scales predictably. DynamoDB: Single-digit millisecond latency at any scale. The capacity model requires understanding, but once configured correctly, it’s remarkably reliable. RDS: Managed PostgreSQL/MySQL with automated backups, failover, and encryption. Removes most of the operational complexity of running databases. CloudWatch: Centralized logging and metrics. The query language takes time to learn, but having logs and metrics in one place simplifies debugging. What requires caution Cost management demands constant attention. AWS pricing is complex, and costs can escalate quickly without monitoring: ...
Developer productivity depends on many factors: environment setup, coding speed, compile times, debugging, testing, deployment, and long-term maintenance. Different languages make different trade-offs across these dimensions. Here’s a comparison based on my experience building production systems: Language Setup Development Debugging Deployment Maintenance Performance C/C++ Complex Slow Challenging Complex Difficult Excellent Java Moderate Moderate Good Moderate Good Good Python Simple Fast Easy Simple Challenging at scale Limited Go Simple Fast Easy Simple Good Good C/C++ C++ offers unmatched control over memory and hardware, which translates to excellent performance when used correctly. However, this comes at a cost: ...
droid.io is an other travis clone. I gave it a try to execute some automated tests on top of cassandra. Unfortunately it doesn’t support cassandra out of the box. But adding support for it is in fact quite easy. ...
Why should you spend time learning go ? ...
Dear C Developers, I’ve spent a good amount of time integrating various C codebases into production-ready software. It’s an interesting challenge, but I often face a few common pain points during the initial stages. Here are some tips that I believe will save us all a lot of time and headaches. Improve Your Logs Many C developers still rely on basic fprintf(stderr, ...) for logging errors. Consider using a more sophisticated logging approach, such as this one. At a minimum, include __FILE__ and __LINE__ macros to make debugging easier for everyone. ...
The developer are the ones who build thing that run by themselves (which they should be proud of, they are makers). But they often forget about automatizing the build process. Some people like to do the same task over and over, if these people happen to be developers, they should be fired. Frequently following the same tasks to reach a goal is something that a computer can do, that’s what they were invented for. And if you spend time doing it, you should be fired. ...